Predicting Healthcare Costs of Diabetes Using Machine Learning Models

Javier - Leonardo Gonzalez - Rodriguez, Carlos Alberto Franco Franco, Olga LUcía Pinzón Espitia, Vincent Augusto

Resultado de la investigación: Contribución a una conferenciaPóster

Resumen

bjectives
To design a predictive model of the in-hospital pharmaceutical expenditure of type II diabetes mellitus, derived from the risk index determined by the associated comorbidities, in a high-complexity hospital in Colombia.

Methods
Data are collected from patients hospitalized in the years 2017 and 2018 at one University Hospital in Bogotá. A cross-sectional descriptive study of costs by typology is performed, then the total risk index derived from the number and severity of comorbidities is calculated, and its results from laboratory indicators. Factorial analysis establishes the prevalence of co-occurrence of chronic disease and associated morbidities. Based on these data, we use different Machine Learning models (such as logistic regression and supervised learning/random forest) that allows to identify the main variables related with the prediction of the pharmaceutical cost or expenditure.

Results
Diabetic patients account for approximately 25% and have an average stay of 5 days. Differences in the number of comorbidities and variability of clinical severity were observed. Laboratory results were available for indicators such as: glycosylated haemoglobin, creatinine and uric acid, as well as lipid profile and 24-hour mean glycemia. The total estimated expense for diabetic patients, with and without associated comorbidities, was US$ 2,204,860 (30.1%) by 2017 and US$ 2,192,365 (27.2%) by 2018. The main co-morbidities associated with diabetes are: Urinary Tract Infection, Hypertension, Heart Failure and Coronary Ischemic Disease. There is an observable relationship in the increasing of costs with the number of associated comorbidities.

Conclusions
Based on the aforementioned data, simulations are being carried out for the development of the model, which is expected to be able to predict the variations that emerge from the existing relationship between the risk index and pharmaceutical expenditure.
Idioma originalEspañol (Colombia)
EstadoPublicada - nov 3 2019
EventoDigital Transformation of Healthcare: Changing Roles and
Sharing Responsibilities : ISPOR EUROPE 2019
- International Society for Pharmacoeconomics and Outcomes Research, Copenhaguen, Dinamarca
Duración: nov 2 2019nov 6 2019

Conferencia

ConferenciaDigital Transformation of Healthcare: Changing Roles and
Sharing Responsibilities
Título abreviadoISPOR 2019
PaísDinamarca
CiudadCopenhaguen
Período11/2/1911/6/19

Citar esto

Gonzalez - Rodriguez, J. . L., Franco Franco, C. A., Pinzón Espitia, O. LU., & Augusto, V. (2019). Predicting Healthcare Costs of Diabetes Using Machine Learning Models. Sesión de cárteles presentada en Digital Transformation of Healthcare: Changing Roles and
Sharing Responsibilities , Copenhaguen, Dinamarca.